Calorimeter shower simulation is a major bottleneck in the Large Hadron Collider computational pipeline. There have been recent efforts to employ deep-generative surrogate models to overcome this challenge. However, many of best-performing models have training and generation times that do not scale well to high-dimensional calorimeter showers. In this work, we introduce supercalo, a flow-based superresolution model, and demonstrate that high-dimensional fine-grained calorimeter showers can be quickly up-sampled from coarse-grained showers. This novel approach presents a way to reduce computational cost, memory requirements, and generation time associated with fast calorimeter simulation models. Additionally, we show that the showers up-sampled by supercalo possess a high degree of variation. This allows a large number of high-dimensional calorimeter showers to be up-sampled from much fewer coarse showers with high fidelity, which results in additional reduction in generation time.

中文翻译：

---

量热仪簇射超分辨率

量热仪簇射模拟是大型强子对撞机计算管道中的主要瓶颈。最近人们努力采用深度生成代理模型来克服这一挑战。然而，许多性能最好的模型的训练和生成时间不能很好地适应高维量热仪簇。在这项工作中，我们引入了Super c alo，一种基于流的超分辨率模型，并证明了高维细粒度量热仪簇可以快速从粗粒度簇中进行上采样。这种新颖的方法提出了一种减少与快速量热计模拟模型相关的计算成本、内存需求和生成时间的方法。此外，我们还表明，由Super Calo上采样的流星雨具有高度的变化。这使得大量高维热量计簇射能够以高保真度从更少的粗簇簇中进行上采样，从而进一步减少生成时间。